Are the CPS Uninsurance Estimates Too High? An Examination of Imputation

Division of Health Policy and Management, University of Minnesota, School of Public Health, 2221 University Avenue, S.E., Minneapolis, MN, USA.
Health Services Research (Impact Factor: 2.78). 11/2007; 42(5):2038-55. DOI: 10.1111/j.1475-6773.2007.00703.x
Source: PubMed

ABSTRACT To determine whether the imputation procedure used to replace missing data by the U.S. Census Bureau produces bias in the estimates of health insurance coverage in the Current Population Survey's (CPS) Annual Social and Economic Supplement (ASEC).
2004 CPS-ASEC.
Eleven percent of the respondents to the monthly CPS do not take the ASEC supplement and the entire supplement for these respondents is imputed by the Census Bureau. We compare the health insurance coverage of these "full-supplement imputations" with those respondents answering the ASEC supplement. We then compare demographic characteristics of the two groups and model the likelihood of having insurance coverage given the data are imputed controlling for demographic characteristics. Finally, in order to gauge the impact of imputation on the uninsurance rate we remove the full-supplement imputations and reweight the data, and we also use the multivariate regression model to simulate what the uninsurance rate would be under the counter-factual simulation that no cases had the full-supplement imputation.
The noninstitutionalized U.S. population under 65 years of age in 2004.
The CPS-ASEC survey was extracted from the U.S. Census Bureau's FTP web page in September of 2004 (
In the 2004 CPS-ASEC, 59.3 percent of the full-supplement imputations under age 65 years had private health insurance coverage as compared with 69.1 percent of the nonfull-supplement imputations. Furthermore, full-supplement imputations have a 26.4 percent uninsurance rate while all others have an uninsurance rate of 16.6 percent. Having imputed data remains a significant predictor of health insurance coverage in multivariate models with demographic controls. Both our reweighting strategy and our counterfactual modeling show that the uninsured rate is approximately one percentage point higher than it should be for people under 65 (i.e., approximately 2.5 million more people are counted as uninsured due to this imputation bias).
The imputed ASEC data are coding too many people to be uninsured. The situation is complicated by the current survey items in the ASEC instrument allowing all members of a household to be assigned coverage with the single press of a button. The Census Bureau should consider altering its imputation specifications and, more importantly, altering how it collects survey data from those who respond to the supplement. IMPLICATIONS FOR POLICY DELIVERY OR PRACTICE: The bias affects many different policy simulations, policy evaluations and federal funding allocations that rely on the CPS-ASEC data.
The Robert Wood Johnson Foundation.

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Available from: Kathleen Call, Sep 04, 2015
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    • "Davern et al. (2007) uncovered a systematic bias introduced in the way in which those cases were imputed. In order to correct for this bias, we removed those cases from the file and reweighted the remaining cases back to the population totals before removing those individuals, as Davern et al. (2007) recommended. In order to correct for disjointed counts resulting from the introduction of new decennial estimates, we averaged the growth between the decades to occur linearly across years. "
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    ABSTRACT: To create a consistent time series to understand coverage trends by harmonizing 20 years of insurance coverage estimates from the Current Population Survey (CPS) that are an available public resource. 1990-2009 CPS Annual Social and Economic Supplement data. CPS data are enhanced to account for methodological and conceptual changes in health insurance measurement and population control totals. The enhancements to the CPS result in an approximately 1 percent reduction in uninsurance rates. Reductions vary over time and by age group. Changes over the last two decades differ slightly using the two data sources. For example, the enhanced data show a greater erosion of private coverage. The enhanced data provide the most consistent measure of health insurance coverage over the past two decades.
    Health Services Research 02/2011; 46(1 Pt 1):199-209. DOI:10.1111/j.1475-6773.2010.01171.x · 2.78 Impact Factor
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    • "The ACS has a better imputation routine for assigning values to people with item-missing data than does the CPS-ASEC. The CPS-ASEC routine is known to produce biases; that is, it produces a smaller number of privately insured and a higher number of uninsured than it should (Davern et al. 2004, 2007). This bias exists in part because the CPS-ASEC routine does not accurately assign dependent coverage. "
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    ABSTRACT: To compare health insurance coverage estimates from the American Community Survey (ACS) to the Current Population Survey (CPS-ASEC). The 2008 ACS and CPS-ASEC, 2009. We compare age-specific national rates for all coverage types and state-level rates of uninsurance and means-tested coverage. We assess differences using t-tests and p-values, which are reported at <.05, <.01, and <.001. An F-test determines whether differences significantly varied by state. Despite substantial design differences, we find only modest differences in coverage estimates between the surveys. National direct purchase and state-level means-tested coverage levels for children show the largest differences. We suggest that the ACS is well poised to become a useful tool to health services researchers and policy analysts, but that further study is needed to identify sources of error and to quantify its bias.
    Health Services Research 10/2010; 46(1 Pt 1):210-31. DOI:10.1111/j.1475-6773.2010.01193.x · 2.78 Impact Factor
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    ABSTRACT: Objective. To assess the quality of new modeled estimates of health insurance based on a federal survey. Data Sources/Study Setting. The study uses data from the Annual Social and Economic Supplements to the Current Population Survey (CPS ASEC), calendar years 2001-2003. Health insurance estimates for low-income populations are analyzed. Study Design. To assess a method for making estimates for uninsured low-income persons, survey estimates of low-income children are compared with modeled estimates. Inferences can be drawn from this comparison and the method is extended to account for demographic groups. Data Collection. Data for 2001-2002 CPS ASEC were self-tabulated for low-income children aged 0-17. A special tabulation of the CPS ASEC was used to categorize the numbers of uninsured by age, race, sex, and Hispanic origin by low income at the state level. This special tabulation was the underlying data for the model. Principal Findings. The modeled estimates reduce the variance and margin of error substantially compared with the survey estimates. Conclusions. These health insurance estimates are credible and increase the precision for the low-income uninsured population. They have broad uses for policy makers and program administrators who focus on the uninsured in special populations.
    Health Services Research 06/2008; 43(5 Pt 1):1693-707. DOI:10.1111/j.1475-6773.2008.00851.x · 2.78 Impact Factor
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